On objective measures of rule surprisingness

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1510, Issue , 1998, Pages 1-9

  Freitas, Alex A ^a  

^a UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

OBJECTIVE MEASURE; SUBJECTIVE ASPECTS;

DATA MINING;

EID: 84947717182     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0094799     Document Type: Conference Paper

References (14)

1. T.M. Cover and J.A. Thomas. Elements of Information Theory. John Wiley & Sons, 1991.
2. A.P. Danyluk & F.J. Provost. Small disjuncts in action: learning to diagnose errors in the local loop of the telephone network. Proc. 10th Int. Conf. Machine Learning, 81-88, 1993.
3. U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth. From data mining to knowledge discovery: an overview. In: U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy. Advances in Knowledge Discovery & Data Mining, 1-34. AAAI/MIT, 1996.
4. F. Gebhardt. Choosing among competing generalizations. Knowledge Acquisit., 3, 1991, 361-380.
5. C. Glymour, D. Madigan, D. Pregibon and P. Smyth. Statistical themes and lessons for data mining. Data Mining and Knowledge Discovery 1(1), 11-28. 1997.
6. R.C. Holte, L.E. Acker and B.W. Porter. Concept learning and the problem of small disjuncts. Proc. Int. Joint Conf. AI (IJCA1-89), 813-818.
7. M. Kamber & R. Shinghal. Evaluating the interestingness of characteristic rules. Proc. 2nd Int. Conf. Knowledge Discovery & Data Mining, 263-266. AAAI, 1996.
8. B. Liu, W. Hsu and S. Chen. Using general impressions to analyze discovered classification rules. Proc. 3rd Int. Conf. Knowl. Disc. & Data Mining, 31-36. AAAI, 1997.
9. J.A. Major and J.J. Mangano. Selecting among rules induced from a hurricane database. J. Intelligent Information Systems 4(1), Jan./1995, 39-52.
10. G. Piatetsky-Shapiro. Discovery, analysis and presentation of strong rules. In: G. Piatetsky-Shapiro and W.J. Frawley. Knowledge Discovery in Databases, 229-248. AAAI, 1991.
11. F.J. Provost and J.M. Aronis. Scaling up inductive learning with massive parallelism. Machine Learning 23(1), Apr./1996, 33-46.
12. E. H. Simpson. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, series B, 13, 1951, 238-241.
13. K.M. Ting. The problem of small disjuncts: its remedy in decision trees. Proc. lOth Canadian Conf. Artificial Intelligence, 91-97. 1994.
14. Simpson's paradox in real life. The Amer. Statist., 36(1), Feb./1982, 46-48.